ViperEkura
7540acb43e
- replace the per-shape auto tables in the linear backend with an M-banded rule (M in [2,4] on compute capability 8.0+) that measured at the HBM bandwidth floor across every family, and fold the capability check into the capable guard - drop the unreachable swiglu auto shape-table machinery so both backends share one env-mode ladder via the new dispatch.env_mode helper - add __all__ across extension modules, name the rotary registration records, and unify typing to the typing-module style - rewrite test_linear_dispatch.py around behavioral routing assertions and document the M-banded policy in the developer docs - Benchmark: L20 SM89, Python dispatch overhead 2.9us to 1.5us, auto now covers every projection shape at M in [2,4].
2.1 KiB
Decode linear shape benchmark
scripts/tools/benchmark_gemv.py records the F.linear baseline used to decide
whether a BF16 GEMV or small-M kernel should enter automatic inference dispatch.
It does not change model execution or select a custom kernel.
The default matrix covers the AstrAI 1B q/k/v/out projections, MLP up/gate/down,
and LM head for M=1,2,4,8,16,32. Each shape runs in eager and CUDA Graph replay
modes. Results include device-event latency samples, p50/p90/p99, estimated
effective IO bandwidth, and CUDA kernel launches per call.
CUDA_VISIBLE_DEVICES=0 python scripts/tools/benchmark_gemv.py \
--output results/decode_linear.json \
--markdown-output results/decode_linear.md
Use --shape NAME:N:K repeatedly to override the preset and --m-values to
change the decode batch sizes. Compare each GPU architecture only with its own
baseline; do not use absolute A100-versus-L20 numbers as a dispatch criterion.
Keep the raw JSON as the source of truth and generate tables with
--markdown-output rather than transcribing measurements by hand.
For direct A/B coverage of the custom kernel and guarded dispatcher across traditional LLaMA and GPT-NeoX decode shapes, use:
CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python scripts/tools/benchmark_gemv_common.py \
--suite all --family traditional --m 2 4 \
--output results/gemv_common.json
The kernel suite compares the directly callable primitive with F.linear.
Use repeatable --shape-label and --chain-label filters for a focused run.
The synthetic-chain suite alternates ASTRAI_GEMV=0 and auto, includes
dependent MLP work and Python dispatch, and rotates through distinct weights.
Automatic dispatch is keyed on the decode batch size alone (M in [2, 4] on
compute capability 8.0+); use --candidate-mode 1 to characterize a family
before widening that band. The checked-in final evidence always uses auto.
It is deliberately not labeled a whole-model throughput benchmark. Both
suites report median/p90 CUDA-event latency plus maximum absolute error,
relative L2 error, and row-wise argmax parity.